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Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Class F1-Score Voting (HCF1V), Cumulative Class F1-Score Voting (CCF1V), and Enhanced Class F1-Score Voting (ECF1V), each assigning classifier weights based on per-class F1-scores obtained during validation rather than overall performance. The strategies were evaluated through computational simulation on three synthetic non-linear datasets (Gaussian Mixture, Spiral, and Moon) and two real-world medical benchmarks\u2014the Breast Cancer Wisconsin Dataset (BCWD) and the UCI Heart Disease Dataset (UHDD)\u2014using scikit-learn\u2013based classifiers, with statistical significance assessed via Wilcoxon signed-rank tests. ECF1V achieved the highest accuracy across most settings, reaching 98.25% on BCWD and 89.47% on UHDD, outperforming both conventional voting methods and several recently published approaches. These results indicate that class-specific F1-score-based weighting improves ensemble reliability, particularly under class imbalance, supporting its applicability to high-stakes classification tasks such as medical diagnosis.<\/jats:p>","DOI":"10.1038\/s41598-026-67363-7","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T23:36:11Z","timestamp":1787614571000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets"],"prefix":"10.1038","volume":"16","author":[{"given":"Amr M.","family":"Hamed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdel-Fattah","family":"Attia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heba","family":"El-Behery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"issue":"12","key":"67363_CR1","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.3390\/diagnostics11122288","volume":"11","author":"K Su","year":"2021","unstructured":"Su, K. et al. 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